@Article{info:doi/10.2196/63786,
author="Yang, Xiongwen
and Xiao, Yi
and Liu, Di
and Shi, Huiyou
and Deng, Huiyin
and Huang, Jian
and Zhang, Yun
and Liu, Dan
and Liang, Maoli
and Jin, Xing
and Sun, Yongpan
and Yao, Jing
and Zhou, XiaoJiang
and Guo, Wankai
and He, Yang
and Tang, Weijuan
and Xu, Chuan",
title="Enhancing Physician-Patient Communication in Oncology Using GPT-4 Through Simplified Radiology Reports: Multicenter Quantitative Study",
journal="J Med Internet Res",
year="2025",
month="Apr",
day="17",
volume="27",
pages="e63786",
keywords="radiology reports; doctor-patient communication; large language models; oncology; GPT-4",
abstract="Background: Effective physician-patient communication is essential in clinical practice, especially in oncology, where radiology reports play a crucial role. These reports are often filled with technical jargon, making them challenging for patients to understand and affecting their engagement and decision-making. Large language models, such as GPT-4, offer a novel approach to simplifying these reports and potentially enhancing communication and patient outcomes. Objective: We aimed to assess the feasibility and effectiveness of using GPT-4 to simplify oncological radiology reports to improve physician-patient communication. Methods: In a retrospective study approved by the ethics review committees of multiple hospitals, 698 radiology reports for malignant tumors produced between October 2023 and December 2023 were analyzed. In total, 70 (10{\%}) reports were selected to develop templates and scoring scales for GPT-4 to create simplified interpretative radiology reports (IRRs). Radiologists checked the consistency between the original radiology reports and the IRRs, while volunteer family members of patients, all of whom had at least a junior high school education and no medical background, assessed readability. Doctors evaluated communication efficiency through simulated consultations. Results: Transforming original radiology reports into IRRs resulted in clearer reports, with word count increasing from 818.74 to 1025.82 (P<.001), volunteers' reading time decreasing from 674.86 seconds to 589.92 seconds (P<.001), and reading rate increasing from 72.15 words per minute to 104.70 words per minute (P<.001). Physician-patient communication time significantly decreased, from 1116.11 seconds to 745.30 seconds (P<.001), and patient comprehension scores improved from 5.51 to 7.83 (P<.001). Conclusions: This study demonstrates the significant potential of large language models, specifically GPT-4, to facilitate medical communication by simplifying oncological radiology reports. Simplified reports enhance patient understanding and the efficiency of doctor-patient interactions, suggesting a valuable application of artificial intelligence in clinical practice to improve patient outcomes and health care communication. ",
issn="1438-8871",
doi="10.2196/63786",
url="https://www.jmir.org/2025/1/e63786",
url="https://doi.org/10.2196/63786"
}